Abstract: Rapid detection of abnormal data in the dam safety monitoring system(such as coarse difference and alarm value) is significant for the safe operation of dams.But traditional methods are prone to miss the detection of small numerical anomalies thus adversely affect subsequent modeling.In this paper, an abnormal monitoring data detection method based on influcing factor decomposition is proposed.It can extract the significant trends and periods in the monitoring sequence by the rapid wavelet transform and the discrete fourier transform, strip away the influence of environmental factors to construct the remainder sequence, and further accurately determine the abnormal monitoring data retained in the remainder sequence in combination with the idea of small probability events.Finally it accurately detects the abnormal data in the monitoring sequence.The numerical results showed that the proposed method has good practicality and stability, the accuracy rate of abnormal detection of various monitoring sequences is more than 98%,and the average values of precision and recall rate are 93% and 92% respectively, showing certainly improved accuracy and generalization ability compared with the traditional detection methods.